Study on brain image denoising based on dual-channel residual network

Huimin Qu; Haiyan Xie; Qianying Wang

doi:10.1117/12.3025928

8 April 2024 Study on brain image denoising based on dual-channel residual network

Huimin Qu, Haiyan Xie, Qianying Wang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903J (2024) https://doi.org/10.1117/12.3025928
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

As an important means to assist doctors in the diagnosis and treatment of brain diseases, brain images play a crucial role in the field of medicine, and how to remove the noise in brain images so as to obtain clear images is a problem that many scholars are working on. Traditional filtering methods have the problem that the details of the image are easily damaged in the denoising process, with obvious noise residues, and the corresponding filters need to be designed according to different noise types. In this paper, an improved dual-channel residual convolutional neural network model based on a deep convolutional neural network is cited for removing noise from brain images. The experimental results show that the average peak signal-to-noise ratio of this method is improved by 0.19783 dB to 17.92 dB compared to several other methods, the ability to reconstruct clear brain images while effectively removing various levels of random noise has some theoretical implications in the medical field.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huimin Qu, Haiyan Xie, and Qianying Wang "Study on brain image denoising based on dual-channel residual network", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903J (8 April 2024); https://doi.org/10.1117/12.3025928

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KEYWORDS

Brain

Neuroimaging

Image denoising

Image processing

Denoising

Medical imaging

Image quality

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